$22K AI Deal, Zero Code Needed

Twenty-two thousand dollars for a single AI build, plus $1,500 every month after, sounds like the kind of headline you scroll past assuming there’s a catch. In the AI agency world, though, that combo is exactly what veterans quietly chase: a chunky upfront implementation fee coupled with predictable recurring revenue that keeps the lights on between big wins.

At the center of this story is Lis Bueno, a member of Zubair Trabzada’s AI Workshop community, who closed a $22,000 deal to build an AI voice agent for a state unemployment agency, with a $1,500 monthly retainer attached — all with no coding. No custom Python stack, no proprietary LLM fine-tuning pipeline, just smart orchestration of no-code tools and a sharp understanding of a government client’s pain points.

On paper, it looks like a one-off success: a government contract, 25 bidders, 10 finalists, and Lis’ team walking away with the signature. But the mechanics behind that phone call form a blueprint that other AI entrepreneurs can copy: identify a real operational problem, match it with a focused AI voice agent, and package it as a service instead of a one-time gadget.

Government unemployment offices bleed time and money on staff turnover, recruiter burnout, and job seekers who drive in, find parking, and then burn their 30–45 minute coaching slots on basic questions. Lis’ voice agent offers 24/7 access to critical information, interview prep, and Q&A so residents arrive prepared and recruiters use their limited time on actual coaching, not FAQ duty.

That value story justifies the $22,000 build fee: the client is not buying a chatbot, they are buying reduced wait times, fewer wasted appointments, and better outcomes for job seekers at scale. The $1,500 monthly retainer then becomes the insurance policy — continuous monitoring, updates, and improvements to keep the system aligned with policy changes and resident needs.

In the AI agency space, that structure is the gold standard because it aligns incentives. The client pays once for the heavy lift, then a manageable monthly fee to keep the agent reliable, secure, and evolving — while the agency locks in a baseline of recurring revenue that compounds with every new deal.

Most AI pitches start with a shiny demo. Lis Bueno started with a bruised system: a state unemployment agency drowning in staff turnover and recruiter burnout. Recruiters and advisers cycled out so fast that institutional knowledge walked out the door with them, while job seekers waited days for answers about basic services.

For the agency, the pain was measurable. High churn meant constant hiring and training costs, plus inconsistent service quality across offices. Every unanswered call or missed question translated into wasted taxpayer money and an overloaded staff trying to plug gaps with overtime.

Job seekers felt it even harder. Many residents lived far from the city center and had to drive in, fight for parking, and sit through security checks just to get 30–45 minutes with a career coach. That short window often vanished on housekeeping: where to find forms, what to bring, how to prep for an interview.

Lis mapped those frictions into a clear problem statement: people needed 24/7 access to critical information, interview prep, and Q&A before they ever sat down with a recruiter. If residents arrived already briefed, in-person sessions could focus on actual coaching instead of logistics. The agency would get higher-quality outcomes from the same staff hours.

This is the opposite of the default AI-agency mistake. Many founders start with a voice agent or some other trendy tool and then go hunting for a problem that vaguely fits. That tech-first mindset produces “cool demos” that never survive a procurement meeting, let alone win a $22,000 build plus a $1,500 monthly retainer.

Lis treated AI as the last step, not the first. She spent time inside the agency’s reality: understaffed offices, limited appointment slots, residents calling after hours, and a mandate to stretch finite budgets. Only after she understood those operational knots did a no-code voice agent emerge as the obvious lever.

The most valuable AI deals do not come from chasing models or prompts. They come from empathy at scale—a granular understanding of how an organization actually works, where people burn out, and where software can quietly remove friction without demanding anyone change jobs or learn to code.

Call centers go home at 5 p.m. Lis Bueno’s AI voice agent never clocks out. Built for a state unemployment agency, it lives on the phone lines and web, answering calls and chats from job seekers at 2 p.m. or 2 a.m. with the same scripted calm.

Under the hood, the agent behaves like a tireless junior recruiter. It walks residents through mock interview questions, gives feedback on answers, and surfaces tailored tips on resumes, dress code, and follow-up etiquette. It also handles high-volume FAQs: eligibility rules, required documents, appointment scheduling, and how to navigate state portals.

Job seekers can ask natural-language questions such as “What should I bring to my appointment?” or “How do I explain a six‑month employment gap?” and get instant, context-aware responses. The agent pulls from curated knowledge bases, agency guidelines, and templated scripts so answers stay accurate and compliant. Tools in the same category, like Voiceflow: Build Chat and Voice AI Agents Without Code, show how far no coding platforms have pushed this kind of orchestration.

For residents, the upgrade feels like going from DMV lines to on-demand concierge. People who live an hour away no longer burn half a day driving, parking, and waiting just to ask basic questions. Instead, they show up to their 30–45 minute session with a career coach already briefed on process, paperwork, and interview basics.

That prep time matters. When the agent handles the repetitive “What do I click?” and “Where do I go?” queries, human staff can spend limited appointments on strategy: career paths, networking plans, and tailored coaching. Recruiters and advisers escape the burnout loop of answering the same 20 questions a hundred times a week.

For the agency, the ROI is blunt. A $22,000 build plus a $1,500 monthly retainer effectively adds a 24/7 digital staffer that never calls in sick, doesn’t churn, and scales to thousands of simultaneous conversations. Freed from low-value Q&A, human teams can support more residents per day, hit performance metrics with fewer hires, and reallocate budget from constant backfilling to measurable service improvements.

Code used to be the moat. Now the moat looks more like a wiring diagram. Instead of spinning up servers and hand-rolling APIs, builders like Lis Bueno are assembling off-the-shelf pieces: Retell AI for natural-sounding voice, n8n for drag-and-drop workflows, plus a handful of SaaS tools the agency already uses.

Retell AI handles the hard stuff that used to demand a full-stack team: low-latency audio streaming, speech recognition, LLM calls, and phone integration. You specify the script logic, guardrails, and data sources; Retell AI handles the telephony and real-time conversation loop so the voice agent sounds less IVR, more receptionist.

n8n sits behind the scenes as the orchestration layer. Instead of writing functions, you drag nodes that say “HTTP Request,” “IF,” or “Google Sheets,” then connect them with arrows. Each call from the voice agent can trigger an n8n workflow that pulls records, logs outcomes, or sends a follow-up email to a recruiter.

This is the real paradigm shift: you design workflows, not codebases. The “IDE” is a browser canvas where you visually map, for example: - Resident calls Retell AI - Retell AI hits an n8n webhook - n8n queries a jobs database API - Response flows back to the caller in seconds

Barrier to entry drops from “learn Python and DevOps” to “understand APIs and logic.” If you can read API docs, define inputs/outputs, and think through edge cases, you can ship something that looks suspiciously like a custom SaaS product for a government agency.

That flips who gets to build. Business-savvy operators who know how unemployment offices actually run can now design systems that reduce burnout and wait times, without begging engineering for sprint time. The scarce skill becomes strategic thinking: choosing the right tools, defining data flows, and setting guardrails around privacy and compliance.

Programming expertise still matters at scale, but for deals like a $22,000 build plus a $1,500 monthly retainer, the power sits with people who can architect systems, not people who can center a div.

Government work rarely goes to the flashiest pitch; it goes to the bidder who speaks fluent bureaucracy. Lis Bueno’s $22,000 build plus $1,500 monthly retainer survived a gantlet that started with roughly 25 bidders, narrowed to a shortlist of 10, and ended with a single signed contract for a state unemployment agency.

From the start, this was a formal procurement, not a friendly intro. The city issued a public bid, collected proposals, and ran them through a scoring matrix that weighed price, technical merit, security, and past performance. Lis had to route her offer through that process to avoid conflicts of interest from her role on a related board, which meant no shortcuts, no backchannel.

Partnership turned that obstacle into an edge. Inside Zubair Trabzada’s AI Workshop community, Lis teamed up with Pam, a member who lives and breathes government proposals. Pam wasn’t a “nice to have”; she became the difference between a clever tech idea and a compliant, fundable project on paper.

Pam translated Lis’s concept into procurement-native language: outcomes, deliverables, milestones, and risk mitigation. Instead of “cool AI voice agent,” the bid promised measurable gains in: - Reduced recruiter burnout and turnover - 24/7 resident access to job-search support - Shorter, more effective coaching sessions

Government buyers do not want startup swagger; they want predictability. The proposal emphasized service continuity, data handling, and integration with existing workflows, not just conversational AI magic. That positioning immediately separated Lis from lower-priced vendors pitching generic chatbots or vague “AI transformation.”

Lis then weaponized her 22-year background running a managed security service provider. Where competitors talked features, she talked cybersecurity: data privacy, call recording policies, audit trails, and alignment with public-sector compliance standards. For an unemployment agency handling sensitive resident data, that hit the trust nerve.

Her bid framed security as a value multiplier, not an add-on line item. By arguing that a fragile, cheaper system could trigger outages, complaints, or even investigations, she made the higher price feel like an insurance policy for the agency’s reputation. In a stack of 25 bids, many undercut her on cost; few could match the combination of no-code agility, domain-specific language, and enterprise-grade security posture.

That blend—procurement fluency, the right partner, and a security-first story—turned a crowded RFP into a one-winner market.

Pricing splits cleanly into two numbers: $22,000 upfront, $1,500 per month after that. Together, they turn a one-off build into a recurring revenue machine while giving the agency a predictable operating expense.

The $22,000 check is not just for “an AI.” It covers weeks of discovery, process mapping, call-flow design, data integration planning, security review, and stakeholder workshops with recruiters, advisers, and IT. Lis and her partner essentially rebuilt how job seekers interact with the unemployment agency, then wrapped that into a voice agent that can actually survive a government rollout.

On the build side, that fee funds custom prompt engineering, multilingual scripting, conditional logic for edge cases, and integration work across tools like n8n and Retell AI: AI Voice Agent Platform for Phone Call Automation. It also pays for testing: hammering the system with real call transcripts, failure scenarios, and stress tests before any resident dials in.

Implementation is its own cost center. The upfront fee absorbs deployment to production phone lines, coordination with telecom vendors, data privacy hardening, staff training, and internal documentation so government employees know when to lean on the AI and when to escalate.

The $1,500 monthly retainer keeps the agent from decaying the moment policies or labor-market conditions change. That budget covers monitoring KPIs like call completion rate, handoff frequency, average handle time, and caller satisfaction, then tuning prompts and flows to keep those numbers moving in the right direction.

Retainer work also includes: - Ongoing maintenance and bug fixes - Content updates when regulations or programs shift - Performance optimization as volumes spike or new languages roll out - Tool upgrades as the underlying no-code stack adds features

Psychologically, this is value-based pricing, not time-and-materials. The agency spends $22,000 + $18,000 per year, but anchors that against six-figure savings from lower turnover, fewer wasted appointments, reduced call backlogs, and residents who no longer burn hours driving to the city for basic questions.

Once the buyer internalizes that a 24/7 voice agent can offload thousands of low-value calls and free recruiters for high-impact work, $1,500 a month stops feeling like software and starts looking like a staffing solution.

Credibility for a $22,000 build plus a $1,500 monthly retainer does not come from a PDF proposal. Lis Bueno treated the written bid as table stakes, then stacked proof on top: her 22 years running a managed security service provider, prior work with government agencies, and a clear understanding of how unemployment offices actually operate. That track record reframed her as a risk manager, not a risky newcomer with shiny tools.

Skeptical stakeholders did not want to “imagine” an AI; they wanted to hear it. Lis leaned hard on a live demo of a working voice agent—even if it was a prototype wired together with no-code tools. Hearing an agent answer real unemployment questions, route a caller, or walk through interview prep in real time did more than any slide deck to kill the “this is vaporware” objection.

She also borrowed a classic enterprise move: show something similar that already works. When you can say, “Here’s a nearly identical agent we deployed for another office, and here’s how many calls it handled last week,” the conversation shifts from “Will this work?” to “When can we start?” Concrete usage, call volumes, and response times become the proof.

Positioning never centered on Retell AI, n8n, or model versions. Lis spoke like a strategic partner sitting on the same side of the table as the agency director. Her language stayed anchored to outcomes:

• 1Reduce recruiter burnout by 30%
• 2Cut time-to-first-answer from days to seconds
• 3Free up 20–30% of staff hours for complex cases

That framing matters more in government than any tech spec sheet. When you talk about fewer frustrated residents, lower turnover, and better use of taxpayer money, you stop sounding like a vendor selling automation and start sounding like part of the policy team trying to fix a broken system.

Partnership turned a clever prototype into a $22,000 contract plus a $1,500 monthly retainer. Inside Zubair Trabzada’s AI Workshop, Lis Bueno didn’t just find tutorials on Retell AI and n8n; she found Pam, a collaborator who could navigate the slow, rule-bound world of government procurement while Lis obsessed over the product and architecture.

Lis brought 22 years of experience as a managed security service provider, a sharp eye for real pain points like recruiter burnout, and the technical vision to assemble a no-code voice agent stack. Pam matched that with deep knowledge of public-sector bureaucracy: how RFPs are written, how evaluation committees think, and how to translate a speculative AI idea into a compliant, fundable proposal.

Together, they attacked the state unemployment agency bid from both sides. Lis designed a 24/7 information agent that could reduce staff turnover pressure and wasted in-person appointments; Pam structured the response to survive a 25-bidder process, get into the final 10, and then win. That meant aligning to procurement language, security requirements, and measurable outcomes, not just cool AI features.

Trying to own every lane—sales, solution design, marketing, legal, proposal writing—kills most solo operators in this space. Government buyers expect polished documentation, airtight scopes, and procurement-safe pricing models. Miss one of those and your elegant workflow in n8n never leaves the whiteboard.

Finding a partner who closes your gaps starts with the right rooms. Look for: - Paid or curated communities where people share actual contracts, not just screenshots - Members who consistently ship: case studies, Loom demos, live builds, not theory - People whose strengths mirror your weaknesses (e.g., you’re technical, they’re sales or compliance)

Vet them like a client would vet you. Ask for examples of previous deals, proposals, or deployments. Start with a tiny project: a joint discovery call, a shared proposal, or a pilot for a small client. Watch how they communicate under deadline, handle conflict, and talk about risk.

Partnerships that work feel uncomfortably specific. If both of you are “idea people” or both are “closers,” you don’t have a team—you have redundancy.

One state agency sounds modest until you realize it is one of 22 unemployment offices running the same playbook, with the same staffing crisis, inside the same bureaucracy. Lis did not just sell a $22,000 build; she bought an on-ramp into an entire statewide system that already budgets for call centers, training, and turnover.

Once the first voice agent goes live and call metrics improve—shorter wait times, fewer missed appointments, higher satisfaction scores—that deployment becomes an internal case study the state cannot ignore. Procurement teams love proof, and nothing sells faster inside government than “copy what already works in another department.”

Success at agency one unlocks low-friction intros to the others: same scripts, same integrations, same compliance story. Instead of re-architecting, Lis can standardize: - One core n8n workflow - One Retell AI voice agent template - One security and privacy package vetted by state IT

Standardization is where government money compounds. Once a state signs off on a pattern, it often rolls out as a “preferred solution,” with language that nudges or even mandates other agencies to adopt the same stack.

Run the math. Even if only 10 of 22 agencies follow, and each pays a conservative $15,000–$20,000 implementation plus the $1,500 monthly retainer, the original pilot seeds a pipeline that can cross seven figures over a few budget cycles. Add upsells for new languages, analytics, and integrations with case-management systems, and the numbers climb again.

Viewed through that lens, the $22,000 check is not the payoff. It is a paid pilot, a reference architecture, and a Trojan horse into a rigid procurement ecosystem. For anyone eyeing similar deals, resources like How to Build an AI Voice Agent: The Complete Guide show how to design something robust enough to become a statewide standard, not just a one-off experiment.

Start with pain, not prompts. Lis Bueno did not pitch “AI” to a state unemployment agency; she targeted a measurable headache: high staff turnover, recruiter burnout, and job seekers wasting 30–45 minute appointments on basic questions instead of actual coaching.

From there, build a simple, repeatable framework. Step one: identify a high-pain workflow where humans act as expensive switchboards—phone trees, intake calls, status checks, FAQs. Step two: map exactly what information people need, when they need it, and what “success” looks like in numbers: fewer missed calls, shorter queues, higher satisfaction.

Step three: assemble, don’t invent. Lis used no-code tools like Retell AI for the voice layer and n8n for automation instead of writing custom code. Your stack should do four things reliably: - Capture and transcribe calls - Pull and update data from existing systems - Handle logic and routing - Log everything for compliance and QA

Step four: fill your gaps with partners. Lis leaned on Pam from the AI Workshop community to cover technical and delivery depth while she focused on the relationship and problem framing. Partnerships let you chase $20,000+ deals before you personally know every API parameter.

Step five: price like a business, not a freelancer. Lis charged $22,000 upfront for the build and a $1,500 monthly retainer for monitoring, improvements, and support. That structure aligns with how agencies and governments think: one capital expense, one predictable operating line item tied to ongoing performance.

Step six: prove it with specifics, not buzzwords. Lis won a bid against 24 competitors because she described concrete outcomes: 24/7 access to interview prep, multilingual support, fewer wasted trips to the office, and better-prepared candidates for overworked recruiters. Live demos and tailored call flows beat generic “AI transformation” decks every time.

Final rule: pick a lane and stay there. Both Lis and Zubair Trabzada hammer the same advice—ignore the urge to chase every new AI trend. Become the person who reliably delivers one type of solution (like voice agents for public services) instead of a tourist in 10 different niches.

Your move now is not to sign up for another tool; it is to find one problem in your own niche that looks eerily like this agency’s bottleneck. Write down who is burning out, where calls are piling up, and what information people cannot get fast enough—then design your first no-code agent to fix exactly that.

What problem did the $22,000 AI voice agent solve?

It addressed high staff turnover and recruiter burnout at a state unemployment agency by providing job seekers with 24/7 access to information, interview prep, and answers to common questions.

What no-code tools are used to build AI voice agents?

Platforms like Retell AI are used for creating conversational voice AI, while tools like n8n or Voiceflow are used for building the backend logic and automation workflows without coding.

How was this AI service priced to reach $22,000?

The pricing model included a $22,000 one-time fee for the initial design, development, and implementation of the AI agent, plus a $1,500 monthly retainer for ongoing maintenance, support, and updates.

Is it possible to sell high-value AI services without coding skills?

Yes. This case study proves that by focusing on solving specific, high-value business problems and using modern no-code platforms, you can build and sell sophisticated AI solutions without being a developer.